Youth Vaping Crisis: Policy Intervention Failure

ODCE Data from 37 countries shows vaping rates accelerated despite regulatory efforts (2012-2023)

SWDchallenge

2025

This visualization examines the failure of vaping regulations to curb youth adoption rates. Using OECD data from 37 countries, the analysis reveals how policy interventions implemented in 2016 and 2018 failed to prevent the alarming rise in youth vaping, with rates surging from 0.3% to 13.9% over a decade despite regulatory efforts.

Author

Steven Ponce

Published

March 2, 2025

Figure 1: Data visualization showing youth vaping rates from 2012-2023 across 37 countries. The top graph reveals rates increasing from 0.3% to 13.9% despite two policy interventions in 2016 and 2018. The bottom chart quantifies policy failure through three metrics: high annual growth rate (+38.5%), a significant gap between youth and adult usage (7.5 percentage points), and current rates far exceeding target levels (+8.9 percentage points above 5% target).

Steps to Create this Graphic

1. Load Packages & Setup

Show code

if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse,         # Easily Install and Load the 'Tidyverse'
  ggtext,            # Improved Text Rendering Support for 'ggplot2'
  showtext,          # Using Fonts More Easily in R Graphs
  scales,            # Scale Functions for Visualization
  glue,              # Interpreted String Literals
  here,              # A Simpler Way to Find Your Files
  janitor,           # Simple Tools for Examining and Cleaning Dirty Data
  skimr,             # Compact and Flexible Summaries of Data
  patchwork,         # The Composer of Plots
  camcorder          # Record Your Plot History
) 

### |- figure size ---- 
gg_record( 
  dir    = here::here("temp_plots"), 
  device = "png",
  width  = 10,
  height = 12,
  units  = "in",
  dpi    = 320)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code

vaping_raw <- read_csv(
  here::here("data/OECD_use_of_vaping_products.csv")
  ) |> 
  clean_names()

3. Examine the Data

Show code

glimpse(vaping_raw)
skim(vaping_raw)

4. Tidy Data

Show code

vaping_clean <- vaping_raw |>
  # Select only the relevant columns
  select(
    country = reference_area,
    year = time_period,
    age_group = age_2,
    sex = sex_2,
    vaping_percentage = obs_value,
    data_status = observation_status
  ) |>
  # Filter for valid data
  filter(!is.na(vaping_percentage)) |>
  mutate(
    year = as.numeric(year),
    # Clean up the age group labels for better visualization
    age_group = case_when(
      age_group == "15 years or over" ~ "Adults (15+)",
      age_group == "15-24 years" ~ "From 15 to 24 years",
      TRUE ~ age_group
    ),
    # Clean up sex labels
    sex = case_when(
      sex == "_T" ~ "Total",
      sex == "M" ~ "Male",
      sex == "F" ~ "Female",
      TRUE ~ sex
    )
  )

# Get main trend data for youth vaping
youth_trend <- vaping_clean |>
  filter(
    age_group == "From 15 to 24 years",
    sex == "Total"
  ) |>
  group_by(year) |>
  summarize(
    avg_vaping = mean(vaping_percentage, na.rm = TRUE), 
    .groups = "drop")

# Define policies and their implementation dates
policy_years <- c(2016, 2018)
policy_labels <- c("Flavor\nRestrictions", "Age\nVerification")

policy_data <- tibble(
  year = policy_years,
  policy = policy_labels,
  y_pos = c(2, 2)                        
)

# Calculate key metrics from the data for annotations
annotations <- youth_trend |>
  # Calculate year-over-year change
  arrange(year) |>
  mutate(
    prev_value = lag(avg_vaping),
    pct_change = (avg_vaping - prev_value) / prev_value * 100
  ) |>
  # Select key years for annotations 
  filter(year %in% c(2013, 2016, 2019, 2022)) |>
  mutate(
    # Create annotation text with proper placement coordinates
    label = case_when(
      year == 2013 ~ "Initial\nadoption rate",  
      year == 2016 ~ "", 
      year == 2019 ~ "Temporary decline\nafter restrictions",  
      year == 2022 ~ "Significant growth\ndespite interventions",  
      TRUE ~ ""
    ),
    # Adjust label positions 
    x_pos = case_when(
      year == 2013 ~ year + 1.2,
      year == 2019 ~ year + 1.4,
      year == 2022 ~ year - 1.7,
      TRUE ~ year
    ),
    y_pos = case_when(
      year == 2013 ~ avg_vaping,
      year == 2019 ~ avg_vaping,
      year == 2022 ~ avg_vaping,
      TRUE ~ avg_vaping
    ),
    # Calculate growth since policy implementation
    growth_since_policy = if_else(
      year == 2022, 
      (avg_vaping / youth_trend$avg_vaping[youth_trend$year == 2016] - 1) * 100,
      NA_real_
    )
  )

# Calculate the growth percentage for 2022 annotation
growth_pct <- round(annotations$growth_since_policy[annotations$year == 2022], 0)

# Calculate growth rates by period
period_growth <- youth_trend |>
  mutate(
    period = case_when(
      year <= 2016 ~ "Pre-Policy (2012-2016)",
      year > 2016 & year <= 2018 ~ "Initial Response (2016-2018)",
      year > 2018 ~ "Post-Implementation (2018-2022)",
      TRUE ~ "Other"
    )
  ) |>
  group_by(period) |>
  summarize(
    start_year = min(year),
    end_year = max(year),
    start_value = first(avg_vaping),
    end_value = last(avg_vaping),
    # Calculate annualized growth rate for fair comparison
    years_elapsed = end_year - start_year,
    total_growth = (end_value / start_value - 1) * 100,
    annual_growth = (((end_value / start_value)^(1/years_elapsed)) - 1) * 100,
    .groups = "drop"
  ) |>
  # Filter out any incomplete periods
  filter(years_elapsed > 0) |>
  # Add a period order for plotting
  mutate(period_order = case_when(
    period == "Pre-Policy (2012-2016)" ~ 1,
    period == "Initial Response (2016-2018)" ~ 2,
    period == "Post-Implementation (2018-2022)" ~ 3,
    TRUE ~ 4
  )) |>
  arrange(period_order)

# Calculate age group gap over time
age_gap_data <- vaping_clean |>
  filter(sex == "Total") |>
  group_by(year, age_group) |>
  summarize(avg_vaping = mean(vaping_percentage, na.rm = TRUE), .groups = "drop") |>
  pivot_wider(names_from = age_group, values_from = avg_vaping) |>
  rename(
    youth = `From 15 to 24 years`,
    adults = `Adults (15+)`
  ) |>
  mutate(
    gap = youth - adults,
    gap_pct = (youth / adults - 1) * 100
  ) |>
  # Calculate the change in gap size over time
  arrange(year) |>
  mutate(
    period = case_when(
      year <= 2016 ~ "Pre-Policy",
      year > 2016 & year <= 2018 ~ "Initial Response",
      year > 2018 ~ "Post-Implementation",
      TRUE ~ "Other"
    )
  )

# Calculate policy effectiveness metrics
policy_effectiveness <- tibble(
  metric = c(
    "Annual Growth Rate After Policies", 
    "Youth-Adult Gap After Policies", 
    "Difference from Target Rate (5%)"
  ),
  value = c(
    # Annual growth rate after policy implementation (2018-2022)
    period_growth$annual_growth[period_growth$period == "Post-Implementation (2018-2022)"],
    
    # Current youth-adult gap (latest year)
    age_gap_data$gap[age_gap_data$year == max(age_gap_data$year)],
    
    # Difference from a hypothetical target rate of 5% (latest year data)
    youth_trend$avg_vaping[youth_trend$year == max(youth_trend$year)] - 5
  ),
  description = c(
    "Average annual increase in youth vaping\nafter both policies were implemented",
    "Percentage point difference between\nyouth and adult rates in latest data",
    "Amount by which current youth vaping rates\nexceed a hypothetical 5% target"
  )
)

policy_effectiveness <- policy_effectiveness |>  
  mutate(
    # Reorder factors for display
    metric = factor(metric, levels = rev(metric)),
    # Category labels
    metric_label = case_when(
      metric == "Annual Growth Rate After Policies" ~ "Annual Growth Rate\nAfter Policies",
      metric == "Youth-Adult Gap After Policies" ~ "Youth-Adult Gap\nAfter Policies",
      metric == "Difference from Target Rate (5%)" ~ "Difference from\nTarget Rate (5%)",
      TRUE ~ as.character(metric)
    ),
    # Format value labels 
    value_label = case_when(
      metric == "Annual Growth Rate After Policies" ~ paste0("+", round(value, 1), "% annually"),
      metric == "Youth-Adult Gap After Policies" ~ paste0(round(value, 1), " percentage points"),
      metric == "Difference from Target Rate (5%)" ~ paste0("+", round(value, 1), " percentage points"),
      TRUE ~ as.character(round(value, 1))
    )
  )

# Create separate data frames for the description texts
desc_data1 <- policy_effectiveness |>
  filter(metric != "Annual Growth Rate After Policies")

desc_data2 <- policy_effectiveness |>
  filter(metric == "Annual Growth Rate After Policies")

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "#FF4B4B", "#555555", "#333333"
  ))

### |-  titles and caption ----
title_text   <- str_glue("Youth Vaping Crisis: Policy Intervention Failure") 
subtitle_text <- str_glue("ODCE Data from 37 countries shows vaping rates accelerated despite regulatory efforts (2012-2023)")

# Create caption
caption_text <- create_swd_caption(
    year = 2025,
    month = "Mar",
    source_text = "Data Source: OECD (DSD_HEALTH_LVNG@DF_HEALTH_LVNG_VP) Use of vaping products"
  )


# |- fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    
    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey95", linewidth = 0.1),
    
    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 10, b = 10, l = 10),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  P1. Vaping Trend Plot  ----
vaping_tred <- youth_trend |>
  ggplot(aes(x = year, y = avg_vaping)) +
  # Geoms
  geom_hline(yintercept = seq(0, 15, by = 5), color = 'gray90', linewidth = 0.1) +
  geom_vline(xintercept = seq(2012, 2022, by = 2), color = 'gray90', linewidth = 0.1) +
  
  geom_vline(xintercept = policy_years,                                         # Policy intervention lines
             linetype = "dashed", color = colors$palette[2], alpha = 0.7, size = 0.5) +
  
  geom_line(size = 1.2, color = colors$palette[1]) +
  geom_point(size = 3.5, color = colors$palette[1]) +
  geom_point(size = 2, color = "white") +  
  
  geom_text(data = policy_data,                                                 # Policy labels 
            aes(x = year, y = y_pos, label = policy),
            color = colors$palette[2], fontface = "bold", size = 3.5,
            hjust = 0, vjust = 0, nudge_x = 0.05) +
  
  geom_text(aes(label = paste0(format(avg_vaping, digits = 1), "%")),
            vjust = -2.5, hjust = 0.8, color = colors$palette[1], fontface = "bold", size = 3.5) +
  
  # Key trend annotations 
  geom_segment(data = annotations |> filter(label != ""),
               aes(x = year, xend = x_pos, y = avg_vaping, yend = y_pos),
               color = alpha(colors$palette[3], 0.5), size = 0.5, 
               arrow = arrow(length = unit(0.01, "npc"), type = "closed", ends = "first")) +
  
  geom_label(data = annotations |> filter(label != ""),
             aes(x = x_pos, y = y_pos, label = label),
             color = colors$palette[3], size = 3, fontface = "italic",
             fill = alpha("white", 0.9), label.size = 0.5, 
             label.padding = unit(0.5, "lines")) +
  
  geom_hline(yintercept = 5, linetype = "dashed", color = "darkgreen", size = 0.3) +
  annotate("text", x = 2012.5, y = 5.3, label = "Target rate (5%)",             # Target reference line
           color = "darkgreen", hjust = 0, size = 3, fontface = "italic") +
  
  # Scales 
  scale_x_continuous(
    breaks = seq(2012, 2022, by = 2),
    limits = c(2012, 2023),
    ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    breaks = seq(0, 15, by = 5),
    limits = c(0, 15)
  ) +
  coord_cartesian(clip = 'off') +
  
  # Labs
  labs(
    title = "Youth Vaping Crisis: Regulatory Failure",
    subtitle = "Despite policy interventions, youth vaping rates have surged to unprecedented levels",
    x = NULL, 
    y = "Percentage of Youth Using Vaping Products"
  ) 


# P2. Policy Effectiveness Plot ----
effectiveness_plot <- ggplot(policy_effectiveness, aes(x = value, y = metric_label)) +
  # Geoms
  geom_vline(xintercept = seq(0, 40, by = 10), color = "gray90", linewidth = 0.3) +
  geom_col(fill = colors$palette[1], width = 0.7, alpha = 0.8) +
  geom_text(aes(label = value_label), 
            hjust = -0.1, 
            color = colors$palette[3], 
            size = 3.5, 
            fontface = "bold") +
  # Explanatory text 
  geom_text(data = desc_data1,
            aes(x = 10, label = description),
            hjust = 0,
            vjust = 1.8,
            color = colors$palette[2],
            size = 2.8,
            lineheight = 0.9) +
  geom_text(data = desc_data2,
            aes(x = 40, label = description),
            hjust = 0,
            vjust = 1.8,
            color = colors$palette[2],
            size = 2.8,
            lineheight = 0.9) +
  
  # Scales
  scale_x_continuous(
    limits = c(0, max(policy_effectiveness$value) * 1.3),
    breaks = seq(0, 40, by = 10),
    expand = expansion(mult = c(0, 0.1))
  ) +
  coord_cartesian(clip = 'off') +
  
  # Labs
  labs(
    x = NULL,
    y = NULL,
    title = "Policy Effectiveness Metrics",
    subtitle = "Three key indicators show disappointing policy outcomes",
    caption = "Note: All metrics derived from OECD vaping dataset; lower values would indicate policy success"
  ) + 
  
  # Theme
  theme(
    plot.caption = element_text(size = rel(0.5), color = colors$caption, margin = margin(t = 10))
  )

# Combined Plots ----
combined_plot <-vaping_tred / effectiveness_plot +
  plot_layout(heights = c(2, 1)) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size   = rel(2.2),
        family = fonts$title,
        face   = "bold",
        color  = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),   
      plot.subtitle = element_text(
        size   = rel(0.9),
        family = fonts$subtitle,
        color  = colors$subtitle,
        lineheight = 1.2,
        margin = margin(t = 5, b = 5)
      ), 
      plot.caption = element_markdown(
        size   = rel(0.6),
        family = fonts$caption,
        color  = colors$caption,
        hjust  = 0.5,
        margin = margin(t = 10)
      ),
      plot.margin = margin(t = 20, r = 10, b = 20, l = 10),
    )
  )

7. Save

Show code

### |-  plot image ----  

source(here::here("R/image_utils.R"))
save_plot_patchwork(
  combined_plot, type = 'swd', year = 2025, month = 03, 
  width = 10, height = 12
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] camcorder_0.1.0 patchwork_1.3.0 skimr_2.1.5     janitor_2.2.0  
 [5] here_1.0.1      glue_1.8.0      scales_1.3.0    showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.4.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4    compiler_4.4.0    
[17] farver_2.1.2       munsell_0.5.1      repr_1.1.7         codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        crayon_1.5.3      
[25] pillar_1.9.0       magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[29] digest_0.6.37      stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4   
[33] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.3         
[37] magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         withr_3.0.2       
[41] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     bit_4.5.0         
[45] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[49] gridGraphics_0.5-1 rlang_1.1.4        gridtext_0.1.5     Rcpp_1.0.13-1     
[53] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[57] vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[61] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in swd_2025_03.qmd. For the full repository, click here.

10. References

Expand for References

Data Sources:

OECD Use of vaping products: DSD_HEALTH_LVNG@DF_HEALTH_LVNG_VP

--- title: "Youth Vaping Crisis: Policy Intervention Failure" subtitle: "ODCE Data from 37 countries shows vaping rates accelerated despite regulatory efforts (2012-2023)" description: "This visualization examines the failure of vaping regulations to curb youth adoption rates. Using OECD data from 37 countries, the analysis reveals how policy interventions implemented in 2016 and 2018 failed to prevent the alarming rise in youth vaping, with rates surging from 0.3% to 13.9% over a decade despite regulatory efforts." author: "Steven Ponce" date: "2025-03-02" categories: ["SWDchallenge", "2025"] tags: [ "vaping", "public health", "policy analysis", "youth health", "regulatory failure", "data storytelling", "ggplot2", "disappointing results", "trend analysis", "health policy" ] image: "thumbnails/swd_2025_03.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/SWD Challenge/2025/swd_2025_03.html" # description: "Analysis of OECD data reveals how youth vaping rates surged despite regulatory interventions, highlighting a critical public health policy failure" # linkedin: true # twitter: true # email: true --- ![Data visualization showing youth vaping rates from 2012-2023 across 37 countries. The top graph reveals rates increasing from 0.3% to 13.9% despite two policy interventions in 2016 and 2018. The bottom chart quantifies policy failure through three metrics: high annual growth rate (+38.5%), a significant gap between youth and adult usage (7.5 percentage points), and current rates far exceeding target levels (+8.9 percentage points above 5% target).](swd_2025_03.png){#fig-1} ### <mark> __Steps to Create this Graphic__ </mark> #### 1. Load Packages & Setup ```{r} #| label: load if (!require("pacman")) install.packages("pacman") pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs scales, # Scale Functions for Visualization glue, # Interpreted String Literals here, # A Simpler Way to Find Your Files janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data patchwork, # The Composer of Plots camcorder # Record Your Plot History ) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 10, height = 12, units = "in", dpi = 320) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read vaping_raw <- read_csv( here::here("data/OECD_use_of_vaping_products.csv") ) |> clean_names() ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(vaping_raw) skim(vaping_raw) ``` #### 4. Tidy Data ```{r} #| label: tidy vaping_clean <- vaping_raw |> # Select only the relevant columns select( country = reference_area, year = time_period, age_group = age_2, sex = sex_2, vaping_percentage = obs_value, data_status = observation_status ) |> # Filter for valid data filter(!is.na(vaping_percentage)) |> mutate( year = as.numeric(year), # Clean up the age group labels for better visualization age_group = case_when( age_group == "15 years or over" ~ "Adults (15+)", age_group == "15-24 years" ~ "From 15 to 24 years", TRUE ~ age_group ), # Clean up sex labels sex = case_when( sex == "_T" ~ "Total", sex == "M" ~ "Male", sex == "F" ~ "Female", TRUE ~ sex ) ) # Get main trend data for youth vaping youth_trend <- vaping_clean |> filter( age_group == "From 15 to 24 years", sex == "Total" ) |> group_by(year) |> summarize( avg_vaping = mean(vaping_percentage, na.rm = TRUE), .groups = "drop") # Define policies and their implementation dates policy_years <- c(2016, 2018) policy_labels <- c("Flavor\nRestrictions", "Age\nVerification") policy_data <- tibble( year = policy_years, policy = policy_labels, y_pos = c(2, 2) ) # Calculate key metrics from the data for annotations annotations <- youth_trend |> # Calculate year-over-year change arrange(year) |> mutate( prev_value = lag(avg_vaping), pct_change = (avg_vaping - prev_value) / prev_value * 100 ) |> # Select key years for annotations filter(year %in% c(2013, 2016, 2019, 2022)) |> mutate( # Create annotation text with proper placement coordinates label = case_when( year == 2013 ~ "Initial\nadoption rate", year == 2016 ~ "", year == 2019 ~ "Temporary decline\nafter restrictions", year == 2022 ~ "Significant growth\ndespite interventions", TRUE ~ "" ), # Adjust label positions x_pos = case_when( year == 2013 ~ year + 1.2, year == 2019 ~ year + 1.4, year == 2022 ~ year - 1.7, TRUE ~ year ), y_pos = case_when( year == 2013 ~ avg_vaping, year == 2019 ~ avg_vaping, year == 2022 ~ avg_vaping, TRUE ~ avg_vaping ), # Calculate growth since policy implementation growth_since_policy = if_else( year == 2022, (avg_vaping / youth_trend$avg_vaping[youth_trend$year == 2016] - 1) * 100, NA_real_ ) ) # Calculate the growth percentage for 2022 annotation growth_pct <- round(annotations$growth_since_policy[annotations$year == 2022], 0) # Calculate growth rates by period period_growth <- youth_trend |> mutate( period = case_when( year <= 2016 ~ "Pre-Policy (2012-2016)", year > 2016 & year <= 2018 ~ "Initial Response (2016-2018)", year > 2018 ~ "Post-Implementation (2018-2022)", TRUE ~ "Other" ) ) |> group_by(period) |> summarize( start_year = min(year), end_year = max(year), start_value = first(avg_vaping), end_value = last(avg_vaping), # Calculate annualized growth rate for fair comparison years_elapsed = end_year - start_year, total_growth = (end_value / start_value - 1) * 100, annual_growth = (((end_value / start_value)^(1/years_elapsed)) - 1) * 100, .groups = "drop" ) |> # Filter out any incomplete periods filter(years_elapsed > 0) |> # Add a period order for plotting mutate(period_order = case_when( period == "Pre-Policy (2012-2016)" ~ 1, period == "Initial Response (2016-2018)" ~ 2, period == "Post-Implementation (2018-2022)" ~ 3, TRUE ~ 4 )) |> arrange(period_order) # Calculate age group gap over time age_gap_data <- vaping_clean |> filter(sex == "Total") |> group_by(year, age_group) |> summarize(avg_vaping = mean(vaping_percentage, na.rm = TRUE), .groups = "drop") |> pivot_wider(names_from = age_group, values_from = avg_vaping) |> rename( youth = `From 15 to 24 years`, adults = `Adults (15+)` ) |> mutate( gap = youth - adults, gap_pct = (youth / adults - 1) * 100 ) |> # Calculate the change in gap size over time arrange(year) |> mutate( period = case_when( year <= 2016 ~ "Pre-Policy", year > 2016 & year <= 2018 ~ "Initial Response", year > 2018 ~ "Post-Implementation", TRUE ~ "Other" ) ) # Calculate policy effectiveness metrics policy_effectiveness <- tibble( metric = c( "Annual Growth Rate After Policies", "Youth-Adult Gap After Policies", "Difference from Target Rate (5%)" ), value = c( # Annual growth rate after policy implementation (2018-2022) period_growth$annual_growth[period_growth$period == "Post-Implementation (2018-2022)"], # Current youth-adult gap (latest year) age_gap_data$gap[age_gap_data$year == max(age_gap_data$year)], # Difference from a hypothetical target rate of 5% (latest year data) youth_trend$avg_vaping[youth_trend$year == max(youth_trend$year)] - 5 ), description = c( "Average annual increase in youth vaping\nafter both policies were implemented", "Percentage point difference between\nyouth and adult rates in latest data", "Amount by which current youth vaping rates\nexceed a hypothetical 5% target" ) ) policy_effectiveness <- policy_effectiveness |> mutate( # Reorder factors for display metric = factor(metric, levels = rev(metric)), # Category labels metric_label = case_when( metric == "Annual Growth Rate After Policies" ~ "Annual Growth Rate\nAfter Policies", metric == "Youth-Adult Gap After Policies" ~ "Youth-Adult Gap\nAfter Policies", metric == "Difference from Target Rate (5%)" ~ "Difference from\nTarget Rate (5%)", TRUE ~ as.character(metric) ), # Format value labels value_label = case_when( metric == "Annual Growth Rate After Policies" ~ paste0("+", round(value, 1), "% annually"), metric == "Youth-Adult Gap After Policies" ~ paste0(round(value, 1), " percentage points"), metric == "Difference from Target Rate (5%)" ~ paste0("+", round(value, 1), " percentage points"), TRUE ~ as.character(round(value, 1)) ) ) # Create separate data frames for the description texts desc_data1 <- policy_effectiveness |> filter(metric != "Annual Growth Rate After Policies") desc_data2 <- policy_effectiveness |> filter(metric == "Annual Growth Rate After Policies") ``` #### 5. Visualization Parameters ```{r} #| label: params ### |- plot aesthetics ---- # Get base colors with custom palette colors <- get_theme_colors(palette = c( "#FF4B4B", "#555555", "#333333" )) ### |- titles and caption ---- title_text <- str_glue("Youth Vaping Crisis: Policy Intervention Failure") subtitle_text <- str_glue("ODCE Data from 37 countries shows vaping rates accelerated despite regulatory efforts (2012-2023)") # Create caption caption_text <- create_swd_caption( year = 2025, month = "Mar", source_text = "Data Source: OECD (DSD_HEALTH_LVNG@DF_HEALTH_LVNG_VP) Use of vaping products" ) # |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Text styling plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)), plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)), # Axis elements axis.title = element_text(color = colors$text, size = rel(0.8)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_blank(), panel.grid.major = element_line(color = "grey95", linewidth = 0.1), # Legend elements legend.position = "plot", legend.title = element_text(family = fonts$text, size = rel(0.8)), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Plot margins plot.margin = margin(t = 10, r = 10, b = 10, l = 10), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot ### |- P1. Vaping Trend Plot ---- vaping_tred <- youth_trend |> ggplot(aes(x = year, y = avg_vaping)) + # Geoms geom_hline(yintercept = seq(0, 15, by = 5), color = 'gray90', linewidth = 0.1) + geom_vline(xintercept = seq(2012, 2022, by = 2), color = 'gray90', linewidth = 0.1) + geom_vline(xintercept = policy_years, # Policy intervention lines linetype = "dashed", color = colors$palette[2], alpha = 0.7, size = 0.5) + geom_line(size = 1.2, color = colors$palette[1]) + geom_point(size = 3.5, color = colors$palette[1]) + geom_point(size = 2, color = "white") + geom_text(data = policy_data, # Policy labels aes(x = year, y = y_pos, label = policy), color = colors$palette[2], fontface = "bold", size = 3.5, hjust = 0, vjust = 0, nudge_x = 0.05) + geom_text(aes(label = paste0(format(avg_vaping, digits = 1), "%")), vjust = -2.5, hjust = 0.8, color = colors$palette[1], fontface = "bold", size = 3.5) + # Key trend annotations geom_segment(data = annotations |> filter(label != ""), aes(x = year, xend = x_pos, y = avg_vaping, yend = y_pos), color = alpha(colors$palette[3], 0.5), size = 0.5, arrow = arrow(length = unit(0.01, "npc"), type = "closed", ends = "first")) + geom_label(data = annotations |> filter(label != ""), aes(x = x_pos, y = y_pos, label = label), color = colors$palette[3], size = 3, fontface = "italic", fill = alpha("white", 0.9), label.size = 0.5, label.padding = unit(0.5, "lines")) + geom_hline(yintercept = 5, linetype = "dashed", color = "darkgreen", size = 0.3) + annotate("text", x = 2012.5, y = 5.3, label = "Target rate (5%)", # Target reference line color = "darkgreen", hjust = 0, size = 3, fontface = "italic") + # Scales scale_x_continuous( breaks = seq(2012, 2022, by = 2), limits = c(2012, 2023), ) + scale_y_continuous( labels = function(x) paste0(x, "%"), breaks = seq(0, 15, by = 5), limits = c(0, 15) ) + coord_cartesian(clip = 'off') + # Labs labs( title = "Youth Vaping Crisis: Regulatory Failure", subtitle = "Despite policy interventions, youth vaping rates have surged to unprecedented levels", x = NULL, y = "Percentage of Youth Using Vaping Products" ) # P2. Policy Effectiveness Plot ---- effectiveness_plot <- ggplot(policy_effectiveness, aes(x = value, y = metric_label)) + # Geoms geom_vline(xintercept = seq(0, 40, by = 10), color = "gray90", linewidth = 0.3) + geom_col(fill = colors$palette[1], width = 0.7, alpha = 0.8) + geom_text(aes(label = value_label), hjust = -0.1, color = colors$palette[3], size = 3.5, fontface = "bold") + # Explanatory text geom_text(data = desc_data1, aes(x = 10, label = description), hjust = 0, vjust = 1.8, color = colors$palette[2], size = 2.8, lineheight = 0.9) + geom_text(data = desc_data2, aes(x = 40, label = description), hjust = 0, vjust = 1.8, color = colors$palette[2], size = 2.8, lineheight = 0.9) + # Scales scale_x_continuous( limits = c(0, max(policy_effectiveness$value) * 1.3), breaks = seq(0, 40, by = 10), expand = expansion(mult = c(0, 0.1)) ) + coord_cartesian(clip = 'off') + # Labs labs( x = NULL, y = NULL, title = "Policy Effectiveness Metrics", subtitle = "Three key indicators show disappointing policy outcomes", caption = "Note: All metrics derived from OECD vaping dataset; lower values would indicate policy success" ) + # Theme theme( plot.caption = element_text(size = rel(0.5), color = colors$caption, margin = margin(t = 10)) ) # Combined Plots ---- combined_plot <-vaping_tred / effectiveness_plot + plot_layout(heights = c(2, 1)) + plot_annotation( title = title_text, subtitle = subtitle_text, caption = caption_text, theme = theme( plot.title = element_text( size = rel(2.2), family = fonts$title, face = "bold", color = colors$title, lineheight = 1.1, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.9), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 5) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, hjust = 0.5, margin = margin(t = 10) ), plot.margin = margin(t = 20, r = 10, b = 20, l = 10), ) ) ``` #### 7. Save ```{r} #| label: save ### |- plot image ---- source(here::here("R/image_utils.R")) save_plot_patchwork( combined_plot, type = 'swd', year = 2025, month = 03, width = 10, height = 12 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`swd_2025_03.qmd`](https://github.com/poncest/personal-website/tree/master/data_visualizations/SWD%20Challenge/2025/swd_2025_03.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References Data Sources: - OECD Use of vaping products: [`DSD_HEALTH_LVNG@DF_HEALTH_LVNG_VP`](https://data-explorer.oecd.org/vis?lc=en&df[ds]=dsDisseminateFinalDMZ&df[id]=DSD_HEALTH_LVNG%40DF_HEALTH_LVNG_VP&df[ag]=OECD.ELS.HD&dq=.A.....&pd=2012%2C&to[TIME_PERIOD]=false) :::